Data
mushroom

mushroom

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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  • mythbusting_1 OpenML100 study_1 study_123 study_14 study_144 study_15 study_190 study_20 study_34 study_37 study_41 study_50 study_70 trivial uci
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Author: [Jeff Schlimmer](Jeffrey.Schlimmer@a.gp.cs.cmu.edu) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/mushroom) - 1981 Please cite: The Audubon Society Field Guide to North American Mushrooms (1981). G. H. Lincoff (Pres.), New York: Alfred A. Knopf ### Description This dataset describes mushrooms in terms of their physical characteristics. They are classified into: poisonous or edible. ### Source ``` (a) Origin: Mushroom records are drawn from The Audubon Society Field Guide to North American Mushrooms (1981). G. H. Lincoff (Pres.), New York: Alfred A. Knopf (b) Donor: Jeff Schlimmer (Jeffrey.Schlimmer '@' a.gp.cs.cmu.edu) ``` ### Dataset description This dataset includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family. Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter class was combined with the poisonous one. The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like ``leaflets three, let it be'' for Poisonous Oak and Ivy. ### Attributes Information ``` 1. cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s 2. cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s 3. cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r, pink=p,purple=u,red=e,white=w,yellow=y 4. bruises?: bruises=t,no=f 5. odor: almond=a,anise=l,creosote=c,fishy=y,foul=f, musty=m,none=n,pungent=p,spicy=s 6. gill-attachment: attached=a,descending=d,free=f,notched=n 7. gill-spacing: close=c,crowded=w,distant=d 8. gill-size: broad=b,narrow=n 9. gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g, green=r,orange=o,pink=p,purple=u,red=e, white=w,yellow=y 10. stalk-shape: enlarging=e,tapering=t 11. stalk-root: bulbous=b,club=c,cup=u,equal=e, rhizomorphs=z,rooted=r,missing=? 12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s 13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s 14. stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y 15. stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y 16. veil-type: partial=p,universal=u 17. veil-color: brown=n,orange=o,white=w,yellow=y 18. ring-number: none=n,one=o,two=t 19. ring-type: cobwebby=c,evanescent=e,flaring=f,large=l, none=n,pendant=p,sheathing=s,zone=z 20. spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r, orange=o,purple=u,white=w,yellow=y 21. population: abundant=a,clustered=c,numerous=n, scattered=s,several=v,solitary=y 22. habitat: grasses=g,leaves=l,meadows=m,paths=p, urban=u,waste=w,woods=d ``` ### Relevant papers Schlimmer,J.S. (1987). Concept Acquisition Through Representational Adjustment (Technical Report 87-19). Doctoral disseration, Department of Information and Computer Science, University of California, Irvine. Iba,W., Wogulis,J., & Langley,P. (1988). Trading off Simplicity and Coverage in Incremental Concept Learning. In Proceedings of the 5th International Conference on Machine Learning, 73-79. Ann Arbor, Michigan: Morgan Kaufmann. Duch W, Adamczak R, Grabczewski K (1996) Extraction of logical rules from training data using backpropagation networks, in: Proc. of the The 1st Online Workshop on Soft Computing, 19-30.Aug.1996, pp. 25-30, [Web Link] Duch W, Adamczak R, Grabczewski K, Ishikawa M, Ueda H, Extraction of crisp logical rules using constrained backpropagation networks - comparison of two new approaches, in: Proc. of the European Symposium on Artificial Neural Networks (ESANN'97), Bruge, Belgium 16-18.4.1997.

23 features

class (target)nominal2 unique values
0 missing
cap-shapenominal6 unique values
0 missing
cap-surfacenominal4 unique values
0 missing
cap-colornominal10 unique values
0 missing
bruises%3Fnominal2 unique values
0 missing
odornominal9 unique values
0 missing
gill-attachmentnominal2 unique values
0 missing
gill-spacingnominal2 unique values
0 missing
gill-sizenominal2 unique values
0 missing
gill-colornominal12 unique values
0 missing
stalk-shapenominal2 unique values
0 missing
stalk-rootnominal4 unique values
2480 missing
stalk-surface-above-ringnominal4 unique values
0 missing
stalk-surface-below-ringnominal4 unique values
0 missing
stalk-color-above-ringnominal9 unique values
0 missing
stalk-color-below-ringnominal9 unique values
0 missing
veil-typenominal1 unique values
0 missing
veil-colornominal4 unique values
0 missing
ring-numbernominal3 unique values
0 missing
ring-typenominal5 unique values
0 missing
spore-print-colornominal9 unique values
0 missing
populationnominal6 unique values
0 missing
habitatnominal7 unique values
0 missing

107 properties

8124
Number of instances (rows) of the dataset.
23
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
2480
Number of missing values in the dataset.
2480
Number of instances with at least one value missing.
0
Number of numeric attributes.
23
Number of nominal attributes.
0
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
1
The minimal number of distinct values among attributes of the nominal type.
21.74
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
0.91
Maximum mutual information between the nominal attributes and the target attribute.
Minimum skewness among attributes of the numeric type.
30.53
Percentage of instances having missing values.
2.05
Third quartile of entropy among attributes.
0
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
5.04
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
12
The maximum number of distinct values among attributes of the nominal type.
Minimum standard deviation of attributes of the numeric type.
1.33
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.73
Average class difference between consecutive instances.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum skewness among attributes of the numeric type.
48.2
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
3916
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.28
Third quartile of mutual information between the nominal attributes and the target attribute.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.41
Average entropy of the attributes.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.83
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.05
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.2
Average mutual information between the nominal attributes and the target attribute.
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.01
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
6.11
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
5
Number of binary attributes.
0.03
First quartile of mutual information between the nominal attributes and the target attribute.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
5.13
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
3.18
Standard deviation of the number of distinct values among attributes of the nominal type.
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.01
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean standard deviation of attributes of the numeric type.
1.47
Second quartile (Median) of entropy among attributes.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
51.8
Percentage of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
4208
Number of instances belonging to the most frequent class.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
3.03
Maximum entropy among attributes.
Minimum of means among attributes of the numeric type.
0.17
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.11
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.

25 tasks

12254 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
334 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
327 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
209 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 4-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
364 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
208 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - target_feature: class
1305 runs - target_feature: class
1302 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
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